... Abstract This paper builds on Klenner et al. 2018 , which utilized machine learning to understand well-to-well communication ("Frac hits") or fracture-driven interaction (FDI) during hydraulic fracturing operations. This paper introduces an infrastructure that enahances the process for real...

... Abstract Recently machine learning has being extensively deployed for oil and gas industry for improving result and expedite process. However, the black box models do not explain their prediction which considered as a barrier to adopt machine learning. This paper is about optimizing hydraulic...

... fracturing u-net architecture artificial intelligence upstream oil & gas training dataset recognition neural network ball pumpdown seat operation prediction ball seat event recognition algorithm dataset slurry rate machine learning cnn model wellhead pressure computational resource deep...

... of unconventional assets by leveraging big data sculpting, domain-induced feature engineering, and robust and explainable machine learning models with quantified uncertainty. This method unlocks the full potential of a well using completion design parameters optimization that considers all the factors that impact...

... modeling reservoir geomechanics well logging machine learning production monitoring reservoir surveillance reservoir characterization fracturing materials flow in porous media tight gas production forecasting equation of state complex reservoir estimates of resource in place artificial...

... gives fair estimates of fracture spatial evolutions. machine learning reservoir characterization intervención de pozos petroleros production monitoring modeling & simulation artificial intelligence hydraulic fracturing reservoir simulation upstream oil & gas production forecasting...

... intelligence classification bit selection drilling operation machine learning drilling equipment dysfunction indicator metric information baseline mse value strength drilling parameter drillstring design upstream oil & gas dysfunction identification founder point chart dysfunction indicator...

... of uncertainty can also be achieved, which assists in understanding the range of parameters which can be used to successfully match the flowback data. flow in porous media history matching palisade evolver equivalent generation machine learning solver optimization problem Fluid Dynamics iteration...

... with completion sensitivity performed on a multithreaded cluster environment on these wells. Advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear regression, etc. were applied on the data points to create a proxy model for the fracturing...

... Abstract This paper presents the use of machine learning via a multiple linear regression and a neural network to solve the complex problem of optimizing completions and well designs in the Duvernay shale. Solutions were revealed that could save over a million dollars per well, along...

... Upstream Oil & Gas porosity pore throat resistivity equation maturation trajectory reservoir machine learning well logging Aguilera thermal maturity determination Pickett plot permeability Introduction Using the concept of a ‘Total Petroleum System (TPS)' and real data from various...

... machine learning Upstream Oil & Gas fracture length diffusivity equation hydraulic fracture hydraulic fracturing node unconventional reservoir fracture anomalous diffusion phenomenon particle subdiffusion graph porous media permeability assumption Introduction In the last decade...

... the most influential factors influencing the water uptake during shut-in periods after hydraulic fracturing operations. Artificial Intelligence neural network residual saturation imbibition saturation spontaneous imbibition fracture machine learning flow in porous media shale gas Upstream...

.... Artificial Intelligence Upstream Oil & Gas correlation machine learning neural network data preparation fluid modeling equation of state unconventional resource conference hydraulic fracturing classification unsupervised neural network classification model spe csur unconventional resource...

... utilization and interrupt timing. Realtime and historic data is tagged, either automatically, semi-automatically using machine learning, or manually, to create a minute-by-minute timeline of rig operations. Operations are then classified both by operation – steering, reaming, making hole, etc. – and well plan...

... for horizontal wells in tight/shale reservoirs. production control production monitoring Reservoir Surveillance production forecasting Modeling & Simulation hyperbolic model machine learning certainty reserves evaluation workflow Artificial Intelligence complex reservoir hyperbolic decline...

... the gradational variation of multiple subsurface parameters into a continuous map of relative economic value, which can then be used to discuss a multitude of appraisal and development issues. machine learning unconventional resource economics geologic subset Energy Economics Upstream Oil & Gas...

... lift system Reservoir Surveillance complex reservoir centroid information machine learning Artificial Intelligence data-based smart model loading production rate liquid loading neural network criteria prediction algorithm learning algorithm initial centroid liquid loading problem...

.... shale gas Reservoir Characterization machine learning complex reservoir Artificial Intelligence Upstream Oil & Gas water saturation gas production reservoir pressure shale Burgos Basin condensate porosity Pimienta shale Texas well habano 1 average reservoir pressure saturation...